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首页> 外文会议>Sentinel-3 for Science Workshop >OCEAN COLOR PRODUCTS SUPPORTING THE ASSESSMENT OF GOOD ENVIRONMENTAL STATUS: DEVELOPMENT OF A SPATIAL DISTRIBUTION MODEL FOR THE SEAGRASS POSIDONIA OCEANICA (L.) DELILE, 1813
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OCEAN COLOR PRODUCTS SUPPORTING THE ASSESSMENT OF GOOD ENVIRONMENTAL STATUS: DEVELOPMENT OF A SPATIAL DISTRIBUTION MODEL FOR THE SEAGRASS POSIDONIA OCEANICA (L.) DELILE, 1813

机译：支持良好环境状况评估的海洋彩色产品：1813年海产波塞冬（POSIDONIA OCEANICA）的空间分布模型的开发

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Posidonia oceanica (L.) Delile, 1813 is a seagrass species endemic to the Mediterranean Sea, which is considered as one of the key habitats of the coastal areas. This species forms large meadows sensitive to several anthropogenic pressures, that can be regarded as indicators of environment quality in coastal environments and its distributional patterns should be take into account when evaluating the Environmental Status following the Ecosystem approach promoted by the Mediterranean Action Plan of UNEP and the EU Marine Strategy Framework Directive (2008/56/EC). The aim of this study was to develop a Species Distribution Model for P. oceanica, to be applied to the whole Mediterranean North African coast, in order to obtain an estimation of the potential distribution of this species in the region to be considered as an indicator for the assessment of good Environmental Status. As the study area is a data-poor zone with regard to seagrass distribution (i.e. only for some areas detailed distribution maps are available), the Species Distribution Model (SDM) was calibrated using high resolution data from 5 Mediterranean sites, located in Italy and Spain and validated using available data from the North African coast. Usually, when developing SDMs species occupancy data is available at coarser resolution than the information of environmental variables, and thus has to be downscaled at the appropriate grain to be coupled to the environmental conditions. Tackling the case of P. oceanica we had to face the opposite problem: the quality (in terms of resolution) of the information on seagrass distribution is generally very high compared to the environmental data available over large scale in marine domains (e.g. global bathymetry data). The high resolution application and the model transfer (from calibration areas to North African coast) was possible taking advantage of Ocean Color products: the probability of presence of the species in a given area was modelled using a binomial generalized linear model as a function of the bathymetry and some water characteristics mainly obtained from satellite data. Full resolution (c.a. 300m) Medium Resolution Imaging Spectrometer (MERIS) sensor imagery have been processed in order to extract a set of environmental variables to be coupled to seagrass distribution in the areas used to calibrate the model and for the whole North Africa coast (i.e. model application area). For the period 2003-2011 we processed data of: 1) the diffuse attenuation coefficient 2) coloured dissolved organic matter 3) Particle backscatter at 443nm; 4) Euphotic depth, estimated considering the coefficient of extinction of light; 5) Euphotic depth/ depth ratio, combining the estimation of euphotic depth with the bathymetry. Other variables have been resampled at MERIS full resolution, like data obtained from Moderate Resolution Imaging Spectroradiometer (MODIS; Sea Surface Temperature and Photosynthetically Available Radiation) or by model simulation (e.g. water salinity). The fitted model suggests that water transparency plays a major role, but also other variables, such as salinity and photosynthetically available radiation at surface, are important at larger spatial scales in explaining meadows distribution. The availability of high resolution time-series of input data allowed us to apply the validated model to the whole NA coast. Using model predictions to identify areas with suitable conditions for P. oceanica, it was possible to develop an indicator of potential habitat use and to define baseline reference conditions, necessary for the assessment of Good Environmental Status in Mediterranean coastal waters. This work shows how the Ocean and Land Colour Instrument (OLCI) within the Sentinel-3 mission can be exploited - thanks to the way opened by MERIS - to carry out the operational monitoring needed for the implementation of the UNEP MAP and EU MSFD Ecosystem Approach to the integrated management of land, water and living resources.
机译：海洋波西多尼亚（L.）Delile，1813年是地中海特有的海草物种，被认为是沿海地区的主要栖息地之一。该物种形成了对几种人为压力敏感的大草甸，可以将其视为沿海环境中环境质量的指标，在​​按照环境署《地中海行动计划》所倡导的生态系统方法评估环境状况时，应考虑其分布模式。欧盟海洋战略框架指令（2008/56 / EC）。这项研究的目的是为海洋假单胞菌建立一个物种分布模型，将其应用到整个北非地中海沿岸，以获得对该物种在该区域的潜在分布的估计，以作为指标。评估良好的环境状况。由于研究区域是关于海草分布的数据贫乏地区（即仅提供某些区域的详细分布图），因此使用来自意大利和意大利的5个地中海站点的高分辨率数据对物种分布模型（SDM）进行了校准。西班牙，并使用来自北非海岸的可用数据进行了验证。通常，在开发SDM时，可以以比环境变量信息更粗糙的分辨率获得物种占用数据，因此必须在适当的谷物上缩小比例以与环境条件耦合。解决海洋假单胞菌的问题，我们不得不面对相反的问题：与海域大规模获得的环境数据（例如全球测深数据）相比，海草分布信息的质量（在分辨率方面）通常非常高）。利用Ocean Color产品可以实现高分辨率应用和模型转换（从校准区域到北非海岸）：使用二项式广义线性模型对给定区域中物种的存在概率进行建模，以得出测深法和一些水特征主要从卫星数据获得。已处理全分辨率（约300m）中分辨率成像光谱仪（MERIS）传感器图像，以便提取一组环境变量，以与用于校准模型的区域以及整个北非海岸地区的海草分布相耦合（即模型应用区域）。在2003-2011年期间，我们处理了以下数据：1）扩散衰减系数2）有色溶解有机物3）粒子在443nm处的反向散射； 4）光合深度，考虑光的消光系数估算； 5）过湿深度/深度比，将过湿深度的估计与测深法相结合。其他变量已以MERIS全分辨率重新采样，例如从中等分辨率成像光谱仪（MODIS；海面温度和光合有效辐射）或通过模型模拟（例如水盐度）获得的数据。拟合模型表明，水的透明度起主要作用，但其他变量（例如盐度和表面光合有效辐射）在更大的空间尺度上对解释草甸分布也很重要。输入数据的高分辨率时间序列的可用性使我们能够将经过验证的模型应用于整个北美海岸。通过使用模型预测来确定适合海洋大洋蟹的条件的区域，有可能开发出潜在的栖息地使用指标并定义基准参考条件，这是评估地中海沿岸水域良好环境状况所必需的。这项工作显示了如何利用Sentinel-3任务中的海洋和陆地颜色仪器（OLCI）-由于MERIS所采用的方式-进行了实施环境署MAP和EU MSFD生态系统方法所需的运行监控土地，水和生物资源的综合管理。

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    Zucchetta M.; Taji M. A.; Mangin A.; Pastres R.;

  • 作者单位

    Department of Environmental Sciences, Informatics and Statistics. CEMAS – Centre for Estuarine and coastal Marine Sciences. University Ca’ Foscari Venice, Italy, Email:matzuc@unive.it;

    University Hassan II-Casablanca, Faculty of sciences Aïn Chock, Morocco ACRI-EC, Casablanca, Morocco;

    ACRI-ST, Sophia-Antipolis, France;

    Department of Environmental Sciences, Informatics and Statistics. CEMAS – Centre for Estuarine and coastal Marine Sciences. University Ca’ Foscari Venice, Italy;

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